Musical Features. We use librosa [1]}, pydub [2]}, and timbral_models of the AudioCommons project [3]} to derive song file musical features. We extract (1) tempo and (2) tempo standard deviation, both in beats per minute (BPM) [4]}; (3) loudness, measured as the average decibels relative to full scale (dBFS) value of the entire song; (4) mode, namely, major or minor, and (5) key, i.e. C# minor [5]}; as well as eight additional timbral features, or the perceived sound qualities of a piece of music. They are, specifically,
(6) depth, related to the emphasis of low frequency content,
(7) brightness, a measure that correlates both with the spectral centroid and the ratio of high frequencies to the sum of all energy of a sound, and
(8) warmth, often created by low and midrange frequencies and associated with lower harmonics [6]};
(9) roughness, a sound's buzzing, harsh, and raspy quality [7]};
(10) sharpness, measuring high frequency content [8]};
(11) hardness, the amount of aggression [9]};
(12) reverberation, a sound or echo's persistence after it is initially produced [10]};
and (13) boominess, a sound's deep resonant quality as measured by the booming index [11]}.
Here, reverberation is classed as a binary variable, while all other timbral features are measured and clipped to values between 0 and 100 following their regression models' training data domain considerations.
